This paper proposes a hybrid multi-object optimization method integrating a uniform design,an adaptive network-based fuzzy inference system(ANFIS),and a multi-objective particle swarm optimizer(MOPSO)to optimize the r...This paper proposes a hybrid multi-object optimization method integrating a uniform design,an adaptive network-based fuzzy inference system(ANFIS),and a multi-objective particle swarm optimizer(MOPSO)to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control(CNC)machines.First,rigid tapping parameters and uniform(including 41-level and 19-level)layouts were adopted to collect representative data for modeling.Next,ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data.In tapping center machines,the synchronization errors and cycle times are important consid-erations,so these two objects were used to build the ANFIS models.Then,a MOPSO algorithm was used to search for the optimal parameter combinations for the two ANFIS models simultaneously.The experimental results showed that the proposed method obtains suitable parameter values and optimal parameter combinations compared with the nonsystematic method.Additionally,the optimal parameter combination was used to optimize existing CNC tools during the commissioning process.Adjusting the proportional and integral gains of the spindle could improve resistance to deformation during rigid tapping.The posi-tion gain and prefeedback coefficient can reduce the synchronization errors significantly,and the acceleration and deceleration times of the spindle affect both the machining time and synchronization errors.The proposed method can quickly and accurately minimize synchronization errors from 107 to 19.5 pulses as well as the processing time from 3,600 to 3,248 ms;it can also shorten the machining time significantly and reduce simultaneous errors to improve tapping yield,there-by helping factories achieve carbon reduction.展开更多
Network densification is envisioned as one of the key enabling technologies in the next generation and beyond wireless networks to satisfy the demand of high coverage and capacity whilst deliver an ultra-reliable low ...Network densification is envisioned as one of the key enabling technologies in the next generation and beyond wireless networks to satisfy the demand of high coverage and capacity whilst deliver an ultra-reliable low latency communication services especially to the users on the move.One of the fundamental tasks in wireless networks is user association.In the case of ultra-dense vehicular networks,due to the dense deployment and small coverage of the eNodeBs,there may be more than one eNodeB that may simultaneously satisfy the conventional maximum radio signal strength user association criteria.In addition to this,the spatial-temporal vehicle distribution in dynamic environments contribute significantly towards the rapidly changing radio environment that substantially impacts the user association,therefore,the network performance and user experience.This paper addresses the problem of user association in dynamic environments by proposing intelligent user association approach,variable-reward,quality-aware Q-learning(VR-QAQL)that has an ability to strike a balance between the number of handovers per transmission and system performance whilst a guaranteed network quality of service is delivered.The VR-QAQL technique integrates the control-theoretic concepts and the reinforcement learning approach in an LTE uplink,using the framework of an urban vehicular environment.The algorithm is assessed using large-scale simulation on a highway scenario at different vehicle speeds in an urban setting.The results demonstrate that the proposed VR-QAQL algorithm outperforms all the other investigated approaches across all mobility levels.展开更多
基金Publication costs are funded by the Ministry of Science and Technology, Taiwan, underGrant Numbers MOST 110-2221-E-153-010.
文摘This paper proposes a hybrid multi-object optimization method integrating a uniform design,an adaptive network-based fuzzy inference system(ANFIS),and a multi-objective particle swarm optimizer(MOPSO)to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control(CNC)machines.First,rigid tapping parameters and uniform(including 41-level and 19-level)layouts were adopted to collect representative data for modeling.Next,ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data.In tapping center machines,the synchronization errors and cycle times are important consid-erations,so these two objects were used to build the ANFIS models.Then,a MOPSO algorithm was used to search for the optimal parameter combinations for the two ANFIS models simultaneously.The experimental results showed that the proposed method obtains suitable parameter values and optimal parameter combinations compared with the nonsystematic method.Additionally,the optimal parameter combination was used to optimize existing CNC tools during the commissioning process.Adjusting the proportional and integral gains of the spindle could improve resistance to deformation during rigid tapping.The posi-tion gain and prefeedback coefficient can reduce the synchronization errors significantly,and the acceleration and deceleration times of the spindle affect both the machining time and synchronization errors.The proposed method can quickly and accurately minimize synchronization errors from 107 to 19.5 pulses as well as the processing time from 3,600 to 3,248 ms;it can also shorten the machining time significantly and reduce simultaneous errors to improve tapping yield,there-by helping factories achieve carbon reduction.
文摘Network densification is envisioned as one of the key enabling technologies in the next generation and beyond wireless networks to satisfy the demand of high coverage and capacity whilst deliver an ultra-reliable low latency communication services especially to the users on the move.One of the fundamental tasks in wireless networks is user association.In the case of ultra-dense vehicular networks,due to the dense deployment and small coverage of the eNodeBs,there may be more than one eNodeB that may simultaneously satisfy the conventional maximum radio signal strength user association criteria.In addition to this,the spatial-temporal vehicle distribution in dynamic environments contribute significantly towards the rapidly changing radio environment that substantially impacts the user association,therefore,the network performance and user experience.This paper addresses the problem of user association in dynamic environments by proposing intelligent user association approach,variable-reward,quality-aware Q-learning(VR-QAQL)that has an ability to strike a balance between the number of handovers per transmission and system performance whilst a guaranteed network quality of service is delivered.The VR-QAQL technique integrates the control-theoretic concepts and the reinforcement learning approach in an LTE uplink,using the framework of an urban vehicular environment.The algorithm is assessed using large-scale simulation on a highway scenario at different vehicle speeds in an urban setting.The results demonstrate that the proposed VR-QAQL algorithm outperforms all the other investigated approaches across all mobility levels.
